Customer Data Privacy Protection Method Based On Singular Value Decomposition Clustering Algorithm

As the information technology develops rapidly, the large-scale personal data such as sensors or IoT (Internet of Things) equipment is kept in the cloud or data centers. Sometimes, the data owner in cloud center needs to publish the data. Therefore, in the face of the risk of personal information leakage, how to take full advantage of data has become a hot research topic. When data is published many times, personal privacy is also disclosed. Thus, this paper puts forward a new clustering algorithm based on singular value decomposition to finish the clustering process. The ideas of distance and information entropy are considered to flexibly adjust data availability and privacy protection in this way. Secondly, this paper also puts forward a dynamic update mechanism to ensure that personal data will not be leaked after multiple releases and minimize information loss. Finally, the effectiveness and superiority of this method are verified by experiments.